• Upgrade transformers dependency, transformers 3.1.0, 3.2.0 and 3.3.1 are working
• Added example code for model distillation: Sentence Embeddings models can be drastically reduced to e.g. only 2-4 layers while keeping 98+% of their performance. Code can be found in examples/training/distillation
• Transformer models can now accepts two inputs ['sentence 1', 'context for sent1'], which are encoded as the two inputs for BERT.

Minor changes:

• Tokenization in the multi-processes encoding setup now happens in the child processes, not in the parent process.
• Added models.Normalize() to allow the normalization of embeddings to unit length